Extended Data Fig. 1: Learning the cis-regulatory code of Drosophila embryo tissues with deep learning.
From: Targeted design of synthetic enhancers for selected tissues in the Drosophila embryo
a) Top: Cartoon with Drosophila embryogenesis and respective stages and times, adapted from ref. 16. Reprinted with permission from AAAS. Bottom: UMAP visualization of cell-x-peak accessibility matrix of cells with inferred age between 10 and 12 h, colored and labeled by tissue annotation. Data from ref. 16. b) Performance of sequence-to-accessibility models for the selected pseudo-bulk tissues from (A). Scatter plots of predicted versus observed DNA accessibility signal (units of log depth-normalized coverage) across DNA sequences in the test set chromosomes (downsampled to 100,000 for easier visualization) for each tissue. Color reflects point density. PCC, Pearson correlation coefficient using all DNA sequences. c) Heatmaps of observed ATAC signal vs predicted ATAC signal across 20,000 sampled differentially accessible regions. The heatmap with observed values is clustered across regions (rows) and tissues (columns). The heatmap with predicted values has the same row and column orders but colored by the predicted values. d) Genome browser screenshot depicting observed and predicted ATAC profiles for the CNS (brown) and somatic muscle (purple) for a locus on the held-out test chromosome. Accessibility peaks for each tissue are shown below the observed signals. High-accessibility regions are highlighted with grey boxes (for example the well-known CNS enhancers upstream of the ftz gene). e) Nucleotide contribution scores for (top) a CNS and (bottom) a somatic muscle enhancer derived from the respective accessibility models. Instances of TF motifs known to be associated with the respective tissues and predicted to be important for the enhancer activity are highlighted.
